Update app.py

app.py

@@ -186,61 +186,80 @@ def process_input(question, table_context):
     return cleaned_sql
 
 # Sample table context examples for the example selector
-example_contexts = [
-    # Example 1
-    """
-CREATE TABLE customers (
-  id INT PRIMARY KEY,
-  name VARCHAR(100),
-  email VARCHAR(100),
-  order_date DATE
-);
-    """,
-    
-    # Example 2
-    """
-CREATE TABLE products (
-  id INT PRIMARY KEY,
-  name VARCHAR(100),
-  category VARCHAR(50),
-  price DECIMAL(10,2),
-  stock_quantity INT
-);
-    """,
-    
-    # Example 3
-    """
-CREATE TABLE employees (
-  id INT PRIMARY KEY,
-  name VARCHAR(100),
-  department VARCHAR(50),
-  salary DECIMAL(10,2),
-  hire_date DATE
-);
-CREATE TABLE departments (
-  id INT PRIMARY KEY,
-  name VARCHAR(50),
-  manager_id INT,
-  budget DECIMAL(15,2)
-);
-    """
-]
+
 
 # Sample question examples
-example_questions = [
-    "Get the names and emails of customers who placed an order in the last 30 days.",
-    "Find all products with less than 10 items in stock.",
-    "List all employees in the Sales department with a salary greater than 50000.",
-    "What is the total budget for departments with more than 5 employees?",
-    "Count how many products are in each category where the price is greater than 100."
-]
+
 
 # Create the Gradio interface
 with gr.Blocks(title="Text to SQL Converter") as demo:
     gr.Markdown("# Text to SQL Query Converter")
     gr.Markdown("Enter your question and optional table context to generate an SQL query.")
     
+    with gr.Row():
+        with gr.Column():
+            question_input = gr.Textbox(
+                label="Your Question",
+                placeholder="e.g., Find all products with price less than $50",
+                lines=2
+            )
+            
+            table_context = gr.Textbox(
+                label="Table Context (Optional)",
+                placeholder="Enter your database schema or table definitions here...",
+                lines=10
+            )
+            
+            submit_btn = gr.Button("Generate SQL Query")
+        
+        with gr.Column():
+            sql_output = gr.Code(
+                label="Generated SQL Query",
+                language="sql",
+                lines=12
+            )
+    
+    # Examples section
+    gr.Markdown("### Try some examples")
+    
+    example_selector = gr.Examples(
+        examples=[
+            ["List all products in the 'Electronics' category with price less than $500", example_contexts[1]],
+            ["Find the total number of employees in each department", example_contexts[2]],
+            ["Get customers who placed orders in the last 7 days", example_contexts[0]],
+            ["Count the number of products in each category", example_contexts[1]],
+            ["Find the average salary by department", example_contexts[2]]
+        ],
+        inputs=[question_input, table_context]
+    )
+    
+    # Set up the submit button to trigger the process_input function
+    submit_btn.click(
+        fn=process_input,
+        inputs=[question_input, table_context],
+        outputs=sql_output
+    )
+    
+    # Also trigger on pressing Enter in the question input
+    question_input.submit(
+        fn=process_input,
+        inputs=[question_input, table_context],
+        outputs=sql_output
+    )
+    
+    # Add information about the model
+    gr.Markdown("""
+    ### About
+    This app uses a fine-tuned language model to convert natural language questions into SQL queries.
     
+    - **Model**: [onkolahmet/Qwen2-0.5B-Instruct-SQL-generator](https://huggingface.co/onkolahmet/Qwen2-0.5B-Instruct-SQL-generator)
+    - **How to use**: 
+      1. Enter your question in natural language
+      2. If you have specific table schemas, add them in the Table Context field
+      3. Click "Generate SQL Query" or press Enter
+      
+    Note: The model works best when table context is provided, but can generate generic SQL queries without it.
+    """)
 
 # Launch the app
 demo.launch()